Noisy Feature Mixup is a generalization of input mixup [1]} and manifold mixup [2]}.
The main novelty of NFM against manifold mixup lies in the injection of noise when taking convex combinations of pairs of input and hidden layer features. Fig. REF  illustrates, at a high level, how this simple modification alters the region in which the resulting augmented data resides.
Fig. REF  shows that NFM can be most effective at smoothing the decision boundary of the trained classifiers; compared to noise injection and mixup alone, it imposes the strongest smoothness on this dataset.
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